使用A/B測試衡量Amazon Personalize推薦結果的有效性

{"type":"doc","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"Original URL: "},{"type":"link","attrs":{"href":"https:\/\/amazonaws-china.com\/cn\/blogs\/machine-learning\/using-a-b-testing-to-measure-the-efficacy-of-recommendations-generated-by-amazon-personalize\/","title":"","type":null},"content":[{"type":"text","text":"https:\/\/aws.amazon.com\/cn\/blogs\/machine-learning\/using-a-b-testing-to-measure-the-efficacy-of-recommendations-generated-by-amazon-personalize\/"}]}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"基於機器學習（ML）的推薦系統早已不是什麼新鮮概念，但開發這類系統仍是一項需要投入大量資源的任務。無論是訓練與推理期間的數據管理，還是運營具備可擴展性的機器學習實時API端點，都着實令人頭痛。"},{"type":"link","attrs":{"href":"https:\/\/amazonaws-china.com\/personalize","title":"","type":null},"content":[{"type":"text","text":"Amazon Personalize"}]},{"type":"text","text":" 將Amazon.com過去二十多年來使用的同一套機器學習技術體系交付至您手中，輕鬆將複雜的個性化功能引入到您的應用程序，且無需任何機器學習專業知識。當前，來自零售、媒體與娛樂、遊戲、旅遊乃至酒店等行業的無數客戶都在使用Amazon Personalize爲用戶提供個性化的內容推薦服務。在Amazon Personalize的幫助下，您可以實現一系列常見用例，包括爲用戶提供個性化商品推薦、顯示相似商品以及根據用戶喜好對商品進行重新排序等。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}}]}